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Application of Artificial Intelligence in Sustainable Energy and Environment Development
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Application of Artificial Intelligence in Sustainable Energy and Environment Development

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 24 October 2024 | Viewed by 7869

Special Issue Editor

Prof. Dr. Wei Pan

E-Mail Website
Guest Editor
School of Applied Economics, Renmin University of China, Beijing 100872, China
Interests: artificial intelligence; digital economy; input–output theory; environment and health; risk management; energy optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I would like to invite you to contribute to a Special Issue of Energies entitled the "Application of Artificial Intelligence in Sustainable Development".

Artificial intelligence (AI) and sustainable development have been hot topics in recent years. AI is transforming our lives at an astonishing rate and sustainable development has become a goal pursued by countries around the world. AI's high intelligence contributes significantly to sustainable development. For example, in the field of energy management, AI can provide support in improving energy efficiency, reducing energy waste, and reducing energy consumption. In terms of environmental monitoring, AI can use a variety of sensor data and a large amount of historical data to accurately predict future environmental changes and provide strong support for environmental protection. In urban planning, AI can combine historical data to adjust urban planning and design, improve the sustainable development capacity of cities, and create a more livable and smarter urban environment. Similarly, driven by AI technology, the field of sustainable development is also constantly innovating and developing. For example, in the construction of smart cities, the intelligent management of urban traffic and the improvement of public safety can be achieved through AI technology to improve the sustainable development capability of cities. At the same time, AI technology can also be used to study and predict major environmental issues, such as climate change. Furthermore, AI's enhancement in energy management and applications, as well as the development of smart cities and communities, drives health-related aspects of sustainable development.

AI is undoubtedly an epochal leap forward in software development. It can continuously store, learn, and analyze data, helping to improve the accuracy of future predictions, and helping more businesses to use big data technology and analyze data in order to gain more strategic understanding. As an important force promoting green energy and sustainable development, AI is leading the progress of sustainable development through the application of energy optimization, intelligent production, energy conservation and emission reduction, new energy research and development, and other aspects. Through innovative approaches, AI is becoming an important catalyst and supporter of sustainable development. Therefore, we will explore in depth how AI can play a role and be applied in sustainable development.

This Special Issue aims to introduce the latest applications of AI technology to sustainable development. Our ultimate goal is to identify the most promising approaches to the current and future challenges of sustainable development and to provide readers with a set of specific applications of AI. These applications are not only conducive to promoting sustainable development, but will also further promote the benefit of artificial intelligence to human society. With the advancement of AI technology, we can expect that it will continue to inject new vitality into sustainable development, contributing to creating a better environment and future for human beings.

Prof. Dr. Wei Pan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI in sustainable development
  • energy
  • environment
  • sustainability
  • artificial intelligence (AI)
  • big data
  • machine learning
  • deep learning

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Published Papers (3 papers)

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Research

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21 pages, 10573 KiB  
Article
Artificial Intelligence and Digital Tools for Assisting Low-Carbon Architectural Design: Merging the Use of Machine Learning, Large Language Models, and Building Information Modeling for Life Cycle Assessment Tool Development
by Mateusz Płoszaj-Mazurek and Elżbieta Ryńska
Energies 2024, 17(12), 2997; https://doi.org/10.3390/en17122997 - 18 Jun 2024
Viewed by 913
Abstract
The construction sector is a significant contributor to global carbon emissions and a major consumer of non-renewable resources. Architectural design decisions play a critical role in a building’s carbon footprint, making it essential to incorporate environmental analyses at various design stages. Integrating artificial [...] Read more.
The construction sector is a significant contributor to global carbon emissions and a major consumer of non-renewable resources. Architectural design decisions play a critical role in a building’s carbon footprint, making it essential to incorporate environmental analyses at various design stages. Integrating artificial intelligence (AI) and building information modeling (BIM) can support designers in achieving low-carbon architectural design. The proposed solution involves the development of a Life Cycle Assessment (LCA) tool. This study presents a novel approach to optimizing the environmental impact of architectural projects. It combines machine learning (ML), large language models (LLMs), and building information modeling (BIM) technologies. The first case studies present specific examples of tools developed for this purpose. The first case study details a machine learning-assisted tool used for estimating carbon footprints during the design phase and shows numerical carbon footprint optimization results. The second case study explores the use of LLMs, specifically ChatGPT, as virtual assistants to suggest optimizations in architectural design and shows tests on the suggestions made by the LLM. The third case study discusses integrating BIM in the form of an IFC file, carbon footprint analysis, and AI into a comprehensive 3D application, emphasizing the importance of AI in enhancing decision-making processes in architectural design. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Energy and Environment Development)
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16 pages, 618 KiB  
Article
An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems
by Chao Tang, Yufeng Zhang, Fan Wu and Zhuo Tang
Energies 2024, 17(10), 2312; https://doi.org/10.3390/en17102312 - 10 May 2024
Cited by 2 | Viewed by 910
Abstract
Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges [...] Read more.
Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Energy and Environment Development)
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Review

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21 pages, 2545 KiB  
Review
Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle
by Christian Nnaemeka Egwim, Hafiz Alaka, Eren Demir, Habeeb Balogun, Razak Olu-Ajayi, Ismail Sulaimon, Godoyon Wusu, Wasiu Yusuf and Adegoke A. Muideen
Energies 2024, 17(1), 182; https://doi.org/10.3390/en17010182 - 28 Dec 2023
Cited by 2 | Viewed by 5450
Abstract
In recent years, there has been a surge in the global digitization of corporate processes and concepts such as digital technology development which is growing at such a quick pace that the construction industry is struggling to catch up with latest developments. A [...] Read more.
In recent years, there has been a surge in the global digitization of corporate processes and concepts such as digital technology development which is growing at such a quick pace that the construction industry is struggling to catch up with latest developments. A formidable digital technology, artificial intelligence (AI), is recognized as an essential element within the paradigm of digital transformation, having been widely adopted across different industries. Also, AI is anticipated to open a slew of new possibilities for how construction projects are designed and built. To obtain a better knowledge of the trend and trajectory of research concerning AI technology application in the construction industry, this research presents an exhaustive systematic review of seventy articles toward AI applicability to the entire lifecycle of the construction value chain identified via the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The review’s findings show foremostly that AI technologies are mostly used in facility management, creating a huge opportunity for the industry to profit by allowing facility managers to take proactive action. Secondly, it shows the potential for design expansion as a key benefit according to most of the selected literature. Finally, it found data augmentation as one of the quickest prospects for technical improvement. This knowledge will assist construction companies across the world in recognizing the efficiency and productivity advantages that AI technologies can provide while helping them make smarter technology investment decisions. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Energy and Environment Development)
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